This article builds Wasserstein ambiguity sets for the unknown probability distribution of
dynamic random variables leveraging noisy partial-state observations. The constructed …

A sudden lane drop on a freeway with high volume of vehicles can cause severe traffic
congestion and safety issues. Forced lane changes near the bottleneck reduce the traffic …

Distributionally robust optimization (DRO) incorporates robustness against uncertainty in the
specification of probabilistic models. This paper focuses on mitigating the curse of …

Ambiguity sets of probability distributions are a prominent tool to hedge against distributional
uncertainty in stochastic optimization. The aim of this paper is to build tight Wasserstein …

This paper studies the characterization of Wasserstein ambiguity sets for dynamic random
variables when noisy partial observations are progressively collected from their evolving …

This paper introduces a spectral parameterization of ambiguity sets to hedge against
distributional uncertainty in stochastic optimization problems. We build an ambiguity set of …

We propose a new framework to solve online optimization and learning problems in
unknown and uncertain dynamical environments. This framework enables us to …

Ambiguity sets of probability distributions are used to hedge against uncertainty about the
true probabilities of uncertain inputs and random quantities of interest (QoIs). When …

Many decision problems in science, engineering, and economics are affected by
uncertainty, which is typically modeled by a random variable governed by an unknown …

The well-known macroscopic Cell Transmission Model (CTM) has been widely used to
develop several Intelligent Transportation Systems (ITS) to mitigate highway traffic …